Summary
In this chapter, we first discussed how to extract value from your data by asking the right analytical questions. Questions may increase in complexity from descriptive, diagnostic, and predictive to prescriptive but may also hold more value. A complexity-value matrix is necessary to prioritize data projects and build a data roadmap. A crucial thing to remember is to capture data as soon as possible, even if you don’t have a data strategy or roadmap yet. All data that you do not capture now cannot be used in the future to extract value from. Next, we introduced a reference architecture diagram. Over time, you will get familiar with every component of the diagram and how they interact with each other.
Four layers of cloud architectures were explained. The ingestion layer is used to pull data into the central cloud data platform. The storage layer is capable of holding massive amounts of data, often in a tiered system, where data gets more business-ready as it moves through the tiers. In the serving layer, the data warehouse is located, which holds data with a strictly enforced schema and is optimized for analytical workloads. Lastly, the consumption layer allows end users and external systems to consume the data in reports and dashboards or to be used in other applications.
Some components of the data platform span across multiple layers. Data orchestration and processing refers to data pipelines that ingest data into the cloud, move data from one place to another, and orchestrate data transformations. Advanced analytics leverages Azure’s many pre-trained ML models and a data science environment to perform complex calculations and provide meaningful predictions. Data governance tools bring data asset compliance, flexible access control, data lineage, and overall insights into the entire data estate. Impeccable security of individual components as well as the integrations between them takes away many of the worries regarding harmful actions being made by third parties. Finally, the extensive monitoring capabilities in Azure allow us to get insights into the health and performance of the processes and data storage in the platform.
Finally, we discussed the drawbacks that on-premises architectures face, such as scalability, cost optimization, agility, and flexibility. These challenges are often conveniently dealt with by leveraging the benefits of cloud-based approaches.
In the next chapter, we will look at two Microsoft frameworks that ease the move to the cloud.
